A Training Method of Average Voice Model for HMM-Based Speech Synthesis

• YAMAGISHI Junichi

  Interdisciplinary Graduate School of Science and Engineering. Tokyo Institute of Technology

• TAMURA Masatsune

  Interdisciplinary Graduate School of Science and Engineering. Tokyo Institute of Technology

• MASUKO Takashi

  Interdisciplinary Graduate School of Science and Engineering. Tokyo Institute of Technology

• TOKUDA Keiichi

  Department of Computer Science, Nagoya Institute of Technology

• KOBAYASHI Takao

  Interdisciplinary Graduate School of Science and Engineering. Tokyo Institute of Technology

抄録

This paper describes a new training method of average voice model for speech synthesis in which arbitrary speaker's voice is generated based on speaker adaptation. When the amount of training data is limited, the distributions of average voice model often have bias depending on speaker and/or gender and this will degrade the quality of synthetic speech. In the proposed method, to reduce the influence of speaker dependence, we incorporate a context clustering technique called shared decision tree context clustering and speaker adaptive training into the training procedure of average voice model. From the results of subjective tests, we show that the average voice model trained using the proposed method generates more natural sounding speech than the conventional average voice model. Moreover, it is shown that voice characteristics and prosodic features of synthetic speech generated from the adapted model using the proposed method are closer to the target speaker than the conventional method.

収録刊行物

• IEICE transactions on fundamentals of electronics, communications and computer sciences

  IEICE transactions on fundamentals of electronics, communications and computer sciences 86 (8), 1956-1963, 2003-08-01

  一般社団法人電子情報通信学会

キーワード

• decision tree
• context clustering
• speaker adaptation
• speaker adaptive training
• average voice model
• HMM-based speech synthesis

詳細情報

• CRID: 1571980077248221568
• NII論文ID: 110003221277
• NII書誌ID: AA10826239
• ISSN: 09168508
• 本文言語コード: en
• データソース種別:
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